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Bioengineering 11 (5): 489 (2024)
Rabkin, Simon
Background: Left ventricular hypertrophy (LVH) is a powerful predictor of future cardiovascular events. Objectives: The objectives of this study were to conduct a systematic review of machine learning (ML) algorithms for the identification of LVH and compare them with respect to the classical features of test sensitivity, specificity, accuracy, ROC and the traditional ECG criteria for LVH. Methods: A search string was constructed with the operators “left ventricular hypertrophy, electrocardiogram” AND machine learning; then, Medline and PubMed were systematically searched. Results: There were 14 studies that examined the detection of LVH utilizing the ECG and utilized at least one ML approach. ML approaches encompassed support vector machines, logistic regression, Random Forest, GLMNet, Gradient Boosting Machine, XGBoost, AdaBoost, ensemble neural networks, convolutional neural networks, deep neural networks and a back-propagation neural network. Sensitivity ranged from 0.29 to 0.966 and specificity ranged from 0.53 to 0.99. A comparison with the classical ECG criteria for LVH was performed in nine studies. ML algorithms were universally more sensitive than the Cornell voltage, Cornell product, Sokolow-Lyons or Romhilt-Estes criteria. However, none of the ML algorithms had meaningfully better specificity, and four were worse. Many of the ML algorithms included a large number of clinical (age, sex, height, weight), laboratory and detailed ECG waveform data (P, QRS and T wave), making them difficult to utilize in a clinical screening situation. Conclusions: There are over a dozen different ML algorithms for the detection of LVH on a 12-lead ECG that use various ECG signal analyses and/or the inclusion of clinical and laboratory variables. Most improved in terms of sensitivity, but most also failed to outperform specificity compared to the classic ECG criteria. ML algorithms should be compared or tested on the same (standard) database.
Citation: Rabkin, S.W. Searching forthe Best Machine Learning Algorithmfor the Detection of Left VentricularHypertrophy from the ECG: AReview. Bioengineering 2024, 11, 489.https://doi.org/10.3390/bioengineering11050489Academic Editor: Giuseppe BaselliReceived: 2 April 2024Revised: 29 April 2024Accepted: 2 May 2024Published: 15 May 2024Copyright: © 2024 by the author.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).bioengineeringReviewSearching for the Best Machine Learning Algorithm for theDetection of Left Ventricular Hypertrophy from the ECG:A ReviewSimon W RabkinDepartment of Medicine, Division of Cardiology, University of British Columbia, 9th Floor 2775 Laurel St.,Vancouver, BC V5Z 1M9, Canada; simon.rabkin@ubc.ca; Tel.: +1-(604)-875-5847; Fax: +1-(604)-875-5849Abstract: Background: Left ventricular hypertrophy (LVH) is a powerful predictor of future cardio-vascular events. Objectives: The objectives of this study were to conduct a systematic review ofmachine learning (ML) algorithms for the identification of LVH and compare them with respect tothe classical features of test sensitivity, specificity, accuracy, ROC and the traditional ECG criteria forLVH. Methods: A search string was constructed with the operators “left ventricular hypertrophy,electrocardiogram” AND machine learning; then, Medline and PubMed were systematically searched.Results: There were 14 studies that examined the detection of LVH utilizing the ECG and utilized atleast one ML approach. ML approaches encompassed support vector machines, logistic regression,Random Forest, GLMNet, Gradient Boosting Machine, XGBoost, AdaBoost, ensemble neural net-works, convolutional neural networks, deep neural networks and a back-propagation neural network.Sensitivity ranged from 0.29 to 0.966 and specificity ranged from 0.53 to 0.99. A comparison withthe classical ECG criteria for LVH was performed in nine studies. ML algorithms were universallymore sensitive than the Cornell voltage, Cornell product, Sokolow-Lyons or Romhilt-Estes criteria.However, none of the ML algorithms had meaningfully better specificity, and four were worse. Manyof the ML algorithms included a large number of clinical (age, sex, height, weight), laboratory anddetailed ECG waveform data (P, QRS and T wave), making them difficult to utilize in a clinicalscreening situation. Conclusions: There are over a dozen different ML algorithms for the detectionof LVH on a 12-lead ECG that use various ECG signal analyses and/or the inclusion of clinicaland laboratory variables. Most improved in terms of sensitivity, but most also failed to outperformspecificity compared to the classic ECG criteria. ML algorithms should be compared or tested on thesame (standard) database.Keywords: left ventricular hypertrophy; electrocardiogram; machine learning; artificial intelligence1. IntroductionA number of different groups have proposed machine learning models to evaluateECG with or without additional clinical and laboratory data to construct an approach toidentify left ventricular hypertrophy (LVH). LVH, or an increased left ventricular mass,is a powerful predictor of future cardiovascular events [1–3]. LVH can serve as a markerfor the severity of (occult) cardiovascular disease, thereby identifying an increased risk ofstroke or, more directly, by limiting myocardial perfusion, leading to myocardial ischemiaand serious cardiac arrhythmias [4–8]. The ECG has been used for decades as an indicatorof the presence of LVH, with increased QRS voltage being considered to be a marker forincreased left ventricular mass [9–16]. Although the ECG QRS voltage criteria are not ahighly sensitive indicator of LVH [17–25], the importance of predicting the presence of LVHand the imperative of cost efficiency, i.e., utilization of a low-cost ECG compared to a moreexpensive echocardiogram or MRI, has focused attention on how to extract more preciseindicators of LVH from ECGs. This imperative is underscored by the use of a 12-lead ECGas part of the basic assessment of patients with cardiovascular disease [26,27].Bioengineering 2024, 11, 489. https://doi.org/10.3390/bioengineering11050489 https://www.mdpi.com/journal/bioengineeringBioengineering 2024, 11, 489 2 of 16Because of the importance of LVH identification, the ML disparate approaches and thevariables considered in each approach, a review of this field has become increasingly needed.A meta-analysis, to construct a single estimate or effect size, is not realistic in fields suchas machine learning, when different input variables and analytic techniques are employedby utilizing different algorithms on different datasets. Hence, an in-depth review is thebest approach to evaluate different ML algorithms. The objectives of this study were toconduct a review of machine learning algorithms for the identification of LVH with respectto the classical features of sensitivity, specificity and accuracy. It also aimed to assess howeach ML algorithm compares to the traditional ECG criteria for LVH, specifically Cornellvoltage [13], Cornell product [28], Sokolow-Lyons [9] and Romhilt-Estes [29] criteria.2. Methods2.1. Literature SearchA search string was constructed using terms connected with Boolean operators “leftventricular hypertrophy AND electrocardiogram or ECG” AND machine learning to iden-tify articles reporting a machine learning approach for the diagnosis of LVH. Medline andPubMed were systematically searched from their date of inception through to 31 October2023. Preferred Reporting Items for Reviews and Meta-Analysis (PRISMA) was used toconduct the search [30] (Supplementary Figure S1).Article titles and abstracts were assessed for full-text review. Papers on hypertrophicobstructive cardiomyopathy were excluded because this entity represents an asymmetriccardiac hypertrophy, which would alter ECG voltage in a different manner. The exclusioncriteria were as follows: non-English studies, non-primary studies, studies without fulltexts, studies that have insufficient data for analysis, non-human studies and studiesunrelated to the investigated topic. The review was not registered, and protocol is notavailable for access.2.2. Data Extraction and ClassificationData extraction was performed by one reviewer. The following items were collectedfrom each article: author, year of publication, recruitment center or clinical trial sampled,sample size, age and sex of participants, definition of LVH and its method of assessment.Reported sensitivity, specificity, positive predictive value, negative predictive value, areaunder the receiver operating curve (ROC), overall accuracy and F1 score were extracted.Input variables and the ML techniques utilized were also extracted.3. ResultsThere were 14 studies that examined the detection of left ventricular hypertrophywith an approach utilizing the ECG and utilized at least one machine language approach(Table 1). Some details of the study population characteristics and input variables aresummarized in Table 1.Lin and Liu evaluated data from 2196 men, aged 17 to 45 years of age, who were inthe military, and used the support vector machine (SVM) classifier as the machine learningmethod [31]. The prevalence of echocardiographic LVH was about 6.5%. Thirty-one inputvariables were utilized that included three clinical ones, age, body height and body weight,and 28 ECG parameters, such as heart rate, the durations of P wave, PR interval, QRSinterval, QT interval and QTc interval in Lead II and the axes of the P, QRS and T waves inLead II, and the voltages of R waves in all Limb Leads I, II, III, aVR, aVL, aVF and S wavein Lead aVL, and the voltages of R and S waves in all precordial leads V1–V6 [31]. Themodel had high sensitivity (86.7%).Bioengineering 2024, 11, 489 3 of 16Table 1. Summary of the studies, input variables and machine learning approaches.Authors Population Country Sample Size Sex (%M) Age (yrs) Method LVH Definition LVH LVH Variables Machine LearningLin & Lui 2020[31] Military Tawain 2196 100 26 Echocardiogram ≥116 g/m2 6.5% 31 parameters 3 clinical -age, bodyheight, body weightSupport vector machineclassifier (SVM)28 ECG parameters: duration P, PR, QRS,QT, QTc, P axis QRS axis, T axis plusR amplitude in all 12 leads, S amplitudein avL, V1-6Sparapani et al.,2019 [32] Multi-ethnic USA 4714 46 MRI 95th percentile NA556 ECG variables: PR interval, P axis,QRS interval, QRS axis plus 552amplitudes and durations per ECGBayesian additive regressiontreeDe laGarza-Salazaret al., 2020 [33]Hospital Mexico 432 56 67 Echocardiogram >115 g/m2 (men) 48%ECG p wave, QRS complex and STwavesC5.0 supervised MLalgorithm to create amultilevel binary decisiontree,>95 g/m2 (women).Kwon et al., 2020[34] Hospital based Korea 21,286 49 59 Echocardiogram>132 g/m2 inmen21%age, sex, weight, height and ECGfeatures, heart rate, presence of atrialfibrillation or flutter, QT, QRS duration,R-wave axis, T-waveENN, LR and RF>109 g/m2 in women‘Raw’ ECG data with 5000 numbersfrom each of the 12 leads.De laGarza-Salazaret al., 2021 [35]Hospital Mexico 439 NA 67 Echocardiogram Presumed sameas 2020 46%ECG variables including T wave voltagein the lead I, peak-to-peak QRS distance(QRS PPK) in aVF, and peak-to-peakQRS distance in aVLC5.0 supervised MLalgorithm to create amultilevel binary decisiontree,Khurshid et al.,2021 [36] UK data base UK 32,239 47 64 MRI 2.6%Sabovcˇik et al.,2021 [37]Generalpopulation Belgium 1407 49 51 Echocardiogram >115 g/m2 (men) 19%67 variables including clinical, ECGonsets, amplitudes and intervals of Pwaves, QRS-complexes, and T wave aswell asLR, XGBoost, Random Forest,AdaBoost, Support VectorMachinesor 95 g/m2(women).blood count, blood glucose, lipid profile,hormones (plasma renin, leptin, insulin,aldosterone, and cortisol), minerals,Angelaki et al.2021 [38] NA Greece 528 44 61 Echocardiogram >115 g/m2 (men) 16.8%clinical variables (sex, age, BMI class,BSA, hypertension, and heightBioengineering 2024, 11, 489 4 of 16Table 1. Cont.Authors Population Country Sample Size Sex (%M) Age (yrs) Method LVH Definition LVH LVH Variables Machine Learning>95 g/m2 (women) 26 chosen ECG-derived features Random ForestLim et al., 2021[39] Military Singapore 17,310 100 18 Echocardiogram >115 g/m2 (men) 0.8%clinical variables were: body weight,height, body fat percentage, and systolicblood pressureLogistic Regression, GLMNet,Random Forests, GradientBoosting MachinesECG variables included: QT interval,mean QRS duration and R wave in lead IZhao et al., 2022[40] Hospital based China 3120 42 65 Echocardiogram >115 g/m2 (men) 56% uncertain CNN>95 g/m2 (women). Lab: Hgb, PLT, lipids, creatinine, Na, KSammani et al.,2022 [41] Hospital basedTheNether-lands2456 55 61 Echocardiogram >115 g/m2 (men) 0.8%age, systolic blood pressure and bodysurface area XGBoost>95 g/m2 (women).20 ECG data: p, QRS and T wave axes,pr, QRS, QT and QTc durations, peakamplitudes of p, Q, R, S and T wavesKokubo et al.,2022 [42] Hospital based Japan 12,008 64 57 Echocardiogram>101 g/m2 formen16.5%19 factors—clinical (age, sex, height andweight) and ECG features (heart rate,rhythm, pr interval, QT interval. QRSaxis, p wave axisENN>85 g/m2 for womenas well as QRS voltages in leads V1, V2,V5 and V6 LR, RFNaderi et al.,2023 [43] UK data base UK 37,534 48 64 MRI >70 g/m2 (men) 1.5%Clinical—blood pressure, diabetesmellitus, lipids, cigarette and alcoholconsumption>55 g/m2 (women) 23 ECG variables from leads I, II, V1-6 LR, SVM, RFLiu et al., 2023[44]MilitaryHospital Tawain 952 90 Echocardiogram >115 g/m2 (men) 18%24 features which consisted of R peakand S valley amplitudes automaticallyobtained from the output of ECG signalDecision tree SVM and Backpropagated Neural NetworkBioengineering 2024, 11, 489 5 of 16Sparapani et al. evaluated 3774 participants from MESA (Multi-Ethnic Study ofAtherosclerosis), free of clinically apparent cardiovascular disease at enrollment, usingECG and participant characteristics to predict LV mass from cardiac magnetic resonanceimaging [32]. There were four global ECG measurements (PR interval, P axis, QRS intervaland QRS axis) plus 552 amplitude and duration measurements per ECG, which resulted in556 ECG variables. The machine learning technique Bayesian Additive Regression Trees(BART) was used [32]. This model showed the highest sensitivity (29.0%), greater than theother criteria, including the Sokolow-Lyon criterion (21.7%), Peguero-Lo Presti (14.5%),Cornell voltage product (10.1%) and Cornell voltage (5.8%). The specificity was >93% forall criteria [32].Garza-Salazar et al. conducted an observational, retrospective case–control study thatincluded data from a representative sample of consecutive adult patients who underwentan ECG and an echocardiogram at their institution [33]. They evaluated 432 patients, ofwhom 47% had LVH [33]. The ECG variables included S-wave voltage and R-wave voltagein all ECG leads (I, II, III, aVL, aVF, aVR and V1-V6), P-wave duration and voltage in theV1 lead, left atrial enlargement, QRS complex duration in lead V1, QRS axis (using leadsI and aVL), intrinsicoid deflection in lead V6 and “ST strain” (downward ST depressionand asymmetric T-wave inversion) [33]. The logistic regression (LR) model was used aswell as a supervised ML algorithm to create a multilevel binary decision tree, using theECG features that provided the greatest information to classify patients as having LVH [33].Their five-level binary decision tree used only six predictive variables and had an accuracyof 71.4%, a sensitivity of 79.6% and specificity of 53% [33].De la Garza Salazar et al. reported another observational, retrospective, case–controlstudy on 439 patients who underwent an echocardiogram and an ECG [35]. Sixteen ECGparameters, including T voltage in lead I, peak-to-peak QRS distance in aVL (>1.235 mV)and peak-to-peak QRS distance in aVF (>0.178 mV), were fed into a C5.0 ML algorithm,a method that defines a decision tree structure model (or criteria). Their model had anaccuracy of 70.5%, a sensitivity of 74.3% and a specificity of 68.7%.Kwon et al. conducted a retrospective cohort study of 12,648 patients who underwent12-lead ECG and echocardiography [34]. LVH was present in 21% of the group. Anensemble neural network (ENN) combining a convolutional (CNN) and deep neuralnetwork (DNN) was developed. Two other machine learning-based algorithms—LR andRF—were also developed. The model was developed using 3162 ECGs from 3162 patients.They used four clinical variables (age, sex, weight and height) and ECG features, suchas heart rate, presence of atrial fibrillation or flutter, QT interval, QTc, QRS duration, R-wave axis and T-wave axis. In addition, they used raw ECG data with 5000 numbersfrom each of the 12 leads. The area under the ROC curve for ENN was 0.880, whichsignificantly outperformed the Romhilt-Estes point system, Cornell voltage criteria and theSokolow-Lyon criteria [34].Lim et al. examined the ECGs and echocardiograms of 17,310 male military conscripts,aged 16 to 23 years [39]. The prevalence of echocardiographic LVH was 0.82%. Severalmachine learning models (Logistic Regression, GLMNet, Random Forests and GradientBoosting Machines) were used. Their clinical variables were body weight, height, bodyfat percentage and systolic blood pressure. Their ECG variables included QT interval,mean QRS duration and R wave in lead I, ECG parameters not used in the classical criteriabut deemed important to the machine learning algorithms, both when ECG parametersalone were included and when all predictive parameters were included. Considering AUC,ML methods achieved superior performance: logistic regression (0.811), GLMNet (0.873),Random Forest (0.824) and Gradient Boosting Machines (0.800).Two studies used the UK Biobank with individuals aged 40 to 69 years, with a meanage of 64 years, of which 52% were female, who had LV mass index assessed by MRI [36].Khurshid et al. also tested a Massachusetts General/Brigham Hospital database, butmore information was available for the UK Biobank so it was selected in this analysis.Khurshid et al. trained an ML model on 32, 239 participants [36]. The input variables wereBioengineering 2024, 11, 489 6 of 16demographic factor, age, sex, race, height, weight and body mass index (BMI), plus ECGwaveform data. Their model had a sensitivity of 34% and a specificity of 96% [36].Naderi et al. also explored the UK Biobank [43]. There was a low prevalence ofLVH, specifically 1.5%. Demographic factors included age, sex and race, and physicalmeasurements included height, weight and body mass index (BMI). Clinical variablesincluded blood pressure, and 23 ECG variables used the independent ECG leads (I, II,V1–6). ECG variables consisted of ECG waveform data. Three supervised machine learningalgorithms, logistic regression (LR), support vector machine (SVM) and Random Forest(RF), were used. For the SVM classifier, the Gaussian kernel function was applied to dealwith potential non-linear data. The three models were comparable in classifying LVH.Classification of LVH with logistic regression had an accuracy of 81%, sensitivity of 70%,specificity of 81% and an AUC of 0.86. Analysis with SVM showed 81% accuracy, sensitivityof 72%, specificity of 81% and AUC of 0.85. RF analysis showed 72% accuracy, sensitivityof 74%, specificity of 72% and AUC of 0.83 [43].Sabovcˇik et al. evaluated 1407 individuals (mean age 51 years, 51% women), randomlyrecruited from the general population, of whom an echocardiographically determinedLV mass was present in 19% [37]. A large number of clinical and laboratory variables(blood count, blood glucose, lipids, renin activity, leptin, insulin, aldosterone and cortisol)were used. From the ECG tracing, the onsets, amplitudes and intervals of P waves, QRScomplexes and T waves were extracted. They used five standard ML methods, XGBoost,AdaBoost, RF, SVM and LR, to build classifiers based on 67 clinical, biochemical andECG variables. A high area under the ROC was found for XGBoost (0.785), RF classifiers(0.783), AdaBoost (0.771), SVM (0.783) and LR (0.783) for predicting LVH. Age, body massindex, different components of blood pressure, history of hypertension, antihypertensivetreatment and various electrocardiographic variables were the top features for predictingLVH [37].Angelaki et al. evaluated 528 patients with and without essential hypertension but noother indications of cardiovascular disease [38]. LVH, assessed by echocardiogram, waspresent in 16.8% of cases. Clinical variables were used. ECG waveform measurementsfrom each lead included peak voltages, area of the QRS complex, planar frontal QRS-Tangle and QTc duration. A Random Forest ML algorithm consisting of a collection of de-correlated decision trees was used. They calculated SHAP (SHapley Additive exPlanations).Hypertension, age and BMI were the most significant factors predicting the presence ofLVH. The area under the QRS complex summed over all 12 leads, the Planar FrontalQRS-T angle and QTc duration, among others, was important in predicting risk. For theidentification of LVH, their model noted 87% accuracy, 75% specificity, 97% sensitivity andarea under the receiver operating curve (AUC/ROC) of 0.91 [38]. However, some of thepatients did not have LVH but rather concentric remodeling [38].Kokubo et al. analyzed data from patients aged 18 years or older, with a mean age64.2 years, 57% men, who had an echocardiogram and ECG at The University of TokyoHospital [42]. LVH was defined as an LVMI > 101 g/m2 for men and > 85 g/m2 forwomen, consistent with recommendations for the Japanese population, and was present in16.5% of cases [42]. The data were derived from a training set of 12,008 persons. Nineteenfactors—clinical (age, sex, height and weight) and ECG features (heart rate, rhythm, printerval, QT interval, QRS axis, P wave axis as well as QRS voltages in leads V1, V2, V5and V6)—were used as input variables. They developed an ensemble neural network(ENN) model, which consisted of a convolutional neural network (CNN) and a deep neuralnetwork (DNN) as well as a LR and RF approaches to detect LVH. For the detection ofLVH, the area under the ROC curve was 0.784 for the deep learning model, which wassignificantly greater than that of the LR, RF or conventional ECG criteria [42].Zhao et al. utilized data from 3120 patients who had an echocardiogram and an ECGwithin one week after hospital admission [40]. The input variables included clinical factors,such as age, sex and medical history; laboratory factors, such as hemoglobin, platelet count,lipids, creatinine, Na, K+; ECG factors, such as R in AVL, V5 and V6, and S in V1 andBioengineering 2024, 11, 489 7 of 16V3. The ECG final dataset included 36,350 ECG segments in the control and LVH groups.They constructed and built a deep learning (DL) model based on convolutional neuralnetwork–long short-term memory (CNN-LSTM) to detect LVH. LVH was predicted by theCNN-LSTM model with an area under the curve (AUC) of 0.62, with a sensitivity of 68%and specificity of 57%. The CNN-LSTM model predicted LVH by 12-lead ECG performedbetter in male than female patients [40].Sammani et al. developed an ML algorithm for echocardiographically detected LVHthat utilized a variety of clinical factors (age, systolic blood pressure and body surface area)and over 20 ECG data variables (P, QRS and T wave axes, PR, QRS, QT and QTc durations,peak amplitudes of P, Q, R, S and T waves in three different ECG leads) [41]. There were26,954 subjects (median age 61 years, 55% male), of whom 0.8% had LVH, and of thosewith LVH, a very small number had amyloidosis; only two had Anderson-Fabry Disease.XGBoost was the only machine learning logarithm used [41].Liu et al. studied 952 individuals, mainly men, from a military hospital and used aback-propagation neural network (BPN) on 24 features, which consisted of R peak and Svalley amplitudes, automatically obtained from the output of the ECG signal. This groupfound a prevalence of 18% with echocardiographic LVH. Their combination of sensitivityand specificity was the highest of any approach [44].Sensitivity and specificity were reported in 13 of the 14 studies. There was a widerange of sensitivity for the ML approaches across all studies. The range is from 0.29to 0.966 (Figure 1). The highest sensitivity was 0.966 using the algorithm proposed byLiu et al., 2023 [44], followed by 0.867 using the algorithm proposed by Lin and Liu [31],followed by the one proposed by De la Garza-Salazar et al. [33]. Specificity ranged from0.53 to 0.99 (Figure 2). The highest specificity was found using the algorithm proposed bySammani et al. [41], followed closely by that of Liu et al. [44] and then Khurshid et al. [36].An overall assessment indicated by AUC was reported in nine studies and rangedfrom 0.705 to 0.89, with the highest AUC reported for the algorithms of Angelaki et al. [38]followed by Kwon et al. [34] (Figure 3). Overall accuracy was detailed in eight studies,with the highest value of 0.961 from Liu et al. [44] followed by that of Angelaki et al. [38](Figure 4). The next best was that of Kwon et al. [34]. Positive and negative predictivevalues were reported in less than one half, or only six studies (Figure 4). Four studiespresented their F1 score, which is a composite indicator of sensitivity, the true-positive rate,taking into account false positives and false negatives. The values ranged from 0.294 [32]to 0.3314 [31] and 0458–0.488 (depending on the ML method) [37] to 0.64, which was thehighest value and was reported by Zhao et al., 2022 [40].Bioengineering 2024, 11, x FOR PEER REVIEW 7 of 17 and V3. The ECG final dataset included 36,350 ECG segments in the control and LVH groups. They constructed and built a deep learning (DL) model based on convolutional neural network–long short-term memory (CNN-LSTM) to detect LVH. LVH was pre-dicted by the CNN-LSTM odel with an area under the curve (AUC) of 0.62, with a sen-sitivity of 68% and specificity of 57%. The CNN-LSTM m del predicted LVH by 12-lead ECG performed better in male than female patients [40]. Sam ani et l. developed a ML algorithm for echocardiographically detected LVH that utilized a variety of clinical factors (age, syst lic blood pressure and body surface are ) and over 20 ECG data vari bles (P, QRS and T wave axes, PR, QRS, QT and QTc durations, peak amplitudes of P, Q, , S and T waves in three different ECG leads) [41]. There were 26,954 subjects (median age 61 years, 55% mal ), of whom 0.8% had LVH, and of those with LVH, a very small number had amyloidosis; only two had Anderson-Fabry Disease. XGBoost was the only machine learning logarithm use [41]. Liu et al. studied 952 individuals, mainly men, from a military hospital and used a back-propagation neural network (BPN) on 24 features, which consisted of R peak and S valley am litudes, automatically obtained from the output of t e ECG signal. This group found a prevalence of 18% with echocardiographic LVH. Their combinatio of sensitivity and specificity was the highest of any appr ach [44]. Sensitivity and specificity were reported in 13 of the 14 studies. There was a wide range of sensitivity for the ML approaches across all studies. The range is from 0.29 to 0.966 (Figure 1). The highest sensitivity was 0.966 using the algorithm proposed by Liu et al. 2023 [44], followed by 0.867 using the algorithm proposed by Lin and Liu [31], followed by the one proposed by De la Garza-Salazar et al. [33]. Specificity ranged from 0.53 to 0.99 (Figure 2). The highest specificity was found using the algorithm proposed by Sammani et al. [41], followed closely by that of Liu et al. [44] and then Khurshid et al. [36]. An overall assessment indicated by AUC was reported in nine studies and ranged from 0.705 to 0.89, with the highest AUC reported for the algorithms of Angelaki et al. [38] followed by Kwon et al. [34] (Figure 3). Overall accuracy was detailed in eight studies, with the highest value of 0.961 from Liu et al. [44] followed by that of Angelaki et al. [38] (Figure 4). The next best was that of Kwon et al. [34]. Positive and negative predictive values were reported in less than one half, or only six studies (Figure 4). Four studies presented their F1 score, which is a composite indicator of sensitivity, the true-positive rate, taking into account false positives and false negatives. The values ranged from 0.294 [32] to 0.3314 [31] and 0458–0.488 (depending on the ML method) [37] to 0.64, which was the highest value and was reported by Zhao et al., 2022 [40]. Figure 1. The sensitivity of the ML algorithms for LVH. Lin & Lui 2020 [31], Sparapani et al., 2019 [32], De la Gar-za-Salazar et al., 2020 [33], Kwon et al., 2020 [34], De la Gar-za-Salazar et al., 2021 Figure 1. The sensitivity of the ML algorithms for LVH. Lin & Lui 2020 [31], Sparapani et al., 2019 [32],De la Gar-za-Salazar et al., 2020 [33], Kwon et al., 2020 [34], De la Gar-za-Salazar et al., 2021 [35],Bioengineering 2024, 11, 489 8 of 16Khurshid et al., 2021 [36], Sabovcˇik et al., 2021 [37], Angelaki et al., 2021 [38], Lim et al., 2021 [39],Zhao et al., 2022 [40], Sammani et al., 2022 [41], Kokubo et al., 2022 [42], Naderi et al., 2023 [43],Liu et al., 2023 [44].Bioengineering 2024, 11, x FOR PEER REVIEW 8 of 17 [35], Khurshid et al., 2021 [36], Sabovčik et al., 2021 [37], Angelaki et al., 2021 [38], Lim et al., 2021 [39], Zhao et al., 202 [40], Sammani et al., 202 [41], Kokubo et al., 2022 [42], Naderi 3 [43], Liu et al., 2023 [44]. Figure 2. The specificity of the ML algorithms for LVH. Lin & Lui 2020 [31], Sparapani et al., 2019 [32], De la Gar-za-Salazar et al., 2020 [33], Kwon et al., 2020 [34], De la Gar-za-Salazar et al., 2021 [35], Khurshid et al., 2021 [36], Sabovčik et al., 2021 [37], Angelaki et al., 2021 [38], Lim et al., 2021 [39], Zhao et al., 2022 [40], Sammani et al., 2022 [41], Kokubo et al., 2022 [42], Naderi et al., 2023 [43], Liu et al., 2023 [44]. Figure 3. The AUC of the ML algorithms for LVH in those studies that reported such data. Lin & Lui 2020 [31], Sparapani et al., 2019 [32], De la Gar-za-Salazar et al., 2020 [33], Kwon et al., 2020 [34], De la Gar-za-Salazar et al., 2021 [35], Khurshid et al., 2021 [36], Sabovčik et al., 2021 [37], Figure 2. The specificity of the ML algorithms for LVH. Lin & Lui 2020 [31], Sparapani et al., 2019 [32],De la Gar-za-Salazar et al., 2020 [33], Kwon et al., 2020 [34], De la Gar-za-Salazar et al., 2021 [35],Khurshid et al., 2021 [36], Sabovcˇik et al., 2021 [37], Angelaki et al., 2021 [38], Lim et al., 2021 [39],Zhao et al., 2022 [40], Sammani et al., 2022 [41], Kokubo et al., 2022 [42], Naderi et al., 2023 [43],Liu et al., 2023 [44].Bioengineering 2024, 11, x FOR PEER REVIEW 8 of 17 [35], Khurshid et al., 2021 [36], Sabovčik et al., 2021 [37], Angelaki et al., 2021 [38], Lim et al., 2021 [ 9], Z ao et al., 2022 [40], Sammani et al., 2022 [41], Kokubo et al., 2022 [4 ], Naderi et al., 20 3 [43], Liu et al., 20 3 [44]. Figure 2. The specificity of the ML algorithms for LVH. Lin & Lui 2020 [31], Sparapani et al., 2019 [32], De la Gar-za-Salazar et al., 2020 [33], Kwon et al., 2020 [34], De la Gar-za-Sal z r et al., 202 [ 5], Khurshid et al., 2021 [36], Sabovčik et al., 2021 [37], Angelaki et l., 2021 [38], Lim et al., 2021 [ 9], Z ao et al., 2022 [40], Sammani et al., 2022 [41], Kokubo t al., 2022 [4 ], Naderi et al., 20 3 [43], Liu et al., 20 3 [44]. Figure 3. The AUC of the ML algorithms for LVH in those studies that reported such data. Lin & Lui 2020 [31], Sparapani et al., 2019 [32], De la Gar-za-Salazar et al., 2020 [33], Kwon et l., 2020 [34], De la Gar-z -Sal zar et al., 2021 [35], Khurshid et al., 2021 [36], Sabovčik et al., 2021 [37], Figure 3. The AUC of the ML algorithms for LVH in those studies that reported such data.Lin & Lui 2020 [31], Sparapani et al., 2019 [32], De la Gar-za-Salazar et al., 2020 [33], Kwon et al.,2020 [34], De la Gar-za-Salazar et al., 2021 [35], Khurshid et al., 2021 [36], Sabovcˇik et al., 2021 [37],Angelaki et al., 2021 [38], Lim et al., 2021 [39], Zhao et al., 2022 [40], Sammani et al., 2022 [41],Kokubo et al., 2022 [42], Naderi et al., 2023 [43].Bioengineering 2024, 11, 489 9 of 16Bioengineering 2024, 11, x FOR PEER REVIEW 9 of 17 Angelaki et al., 2021 [38], Lim et al., 2021 [39], Zhao et al., 2022 [40], Sammani et al., 2022 [41], Ko-kubo et al., 2022 [42], Naderi et al., 2023 [43]. Figure 4. The positive predictive value (PPV), negative predictive value (NPV) and accuracy of the ML algorithms for LVH in those studies that reported such data. Lin & Lui 2020 [31], Sparapani et al., 2019 [32], De la Gar-za-Salazar et al., 2020 [33], Kwon et al., 2020 [34], De la Gar-za-Salazar et al., 2021 [35], Khurshid et al., 2021 [36], Sabovčik et al., 2021 [37], Angelaki et al., 2021 [38], Lim et al., 2021 [39], Zhao et al., 2022 [40], Sammani et al., 2022 [41], Kokubo et al., 2022 [42], Naderi et al., 2023 [43], Liu et al., 2023 [44]. Three studies used different ML algorithms and compared them. Kwon et al. found that their AI algorithm based on ENN significantly outperformed the DNN, CNN, RF and LR ones using AUC as the metric [34]. Using the same metric (AUC), Sabovcik et al. re-ported that XGBoost and RF classifiers exhibited a high area under the receiver operating characteristic curve, with values between 77.7% and 78.5%, for predicting LVH, and these approaches were better than AdaBoost, support vector machines and logistic regression [37]. They did not use an ENN approach. Kokubo et al. found values of 78.4% for the deep learning model (ENN), which was significantly higher than that of the logistic regression and Random Forest methods [42]. Thus, based on the two studies that used ENN, ENN offers a competitive advantage over other ML approaches [34,42]. Nine studies compared their ML approach to the classic ECG approach. The ML al-gorithm of Zhao et al. outperformed Cornell voltage criteria (AUC 0.57, sensitivity 48%, specificity 72%) and Sokolow-Lyon voltage (AUC 0.51, sensitivity 14%, specificity 96%). [40]. The ML algorithm proposed by Liu et al. reported sensitivity, specificity and accu-racy values that were better than the Cornell voltage criteria, Sokolow-Lyons, Peguero, Framingham and Gubner criteria [44]. Of the two ML algorithms presented by De la Garza-Salazar et al., the first had better results than the Romhilt-Estes score, with an accu-racy of 61.3%, a sensitivity of 23.2% and a specificity of 94.8% [33], while the second one had an accuracy better than Romhilt-Estes (57%), Cornell (59%) and Sokolow-Lyon (53.9%) [35]. Figure 4. The positive predictive value (PPV), negative predictive value (NPV) and accuracyof the ML algorithms for LVH in those studies that reported such data. Lin & Lui 2020 [31],Sparapani et al., 2019 [32], De la Gar-za-Salazar et al., 2020 [33], Kwon et al., 2020 [34],De la Gar-za-Salazar et al., 2021 [35], Khurshid et al., 2021 [36], Sabovcˇik et al., 2021 [37],Angelaki et al., 2021 [38], Lim et al., 2021 [39], Zhao et al., 2022 [40], Sammani et al., 2022 [41],Kokubo et al., 2022 [42], Naderi et al., 2023 [43], Liu et al., 2023 [44].Thre studies used different ML algorithms and compared them. Kwon et al. foundthat their AI l rit ENN significantly outperformed the D N, CNN, RFand LR one using AUC as the metric [34]. Using the same metric (AUC), Sabovcik et al.reporte t t i ited a high area under the receiver operatingcharacteristic urve, with values betwe n 7 .7% and 78.5%, for predicting LVH, and theseap roaches wer bett r than AdaBoost, support vector machines and logistic regression [37].They did not use an ENN approach. Kokubo et al. found values of 78.4% for the de plearning model (EN ), which was ignificantly higher than that of the logistic regressionand Random Forest methods [42]. Thus, based on the two studies that used EN , ENoffers a competi ive advantage over other ML ap roaches [34, 2].Nine studies compared their t t l ssic ECG approach. The MLalgorith of Zhao et al. outperformed Cornell voltage criteria (AUC 0.57, sensitivity48%, specificity 72%) and S kolow-Ly n voltage (AUC 0.51, sensitivity 14%, specificity96%). [40]. The ML algorithm proposed by Liu et al. reported sensitivity, specificity andaccuracy values that were better than the Cornell voltage criteria, Sokolo -Lyons, Peguero,Framingham and Gubner criteria [44]. Of the two ML algorithms presented by De la Garza-Salazar et al., the first had better results than the Romhilt-Estes score, with an accuracy of61.3%, a sensitivity of 23.2% and a specificity of 94.8% [33], while the second one had anaccuracy better than Romhilt-Estes (57%), Cornell (59%) and Sokolow-Lyon (53.9%) [35].Eight studies reported better sensitivity for their ML algorithm compared to assess-ment with the Romhilt-Estes point system, Cornell voltage criteria or Sokolow-Lyon criteria(Figure 5). Four of the eight studies reported a specificity of equal to or better than theclassic ECG criteria [32,34,36,42] (Figure 6). For some ML algorithms, specificity was higherthan the classic ECG criteria, while others did not find a significant difference. SevenBioengineering 2024, 11, 489 10 of 16studies reported better AUC for their ML algorithm compared to an assessment with theRomhilt-Estes point system, Cornell voltage criteria or Sokolow-Lyon criteria (Figure 7).Bioengineering 2024, 11, x FOR PEER REVIEW 10 of 17 Eight studies reported better sensitivity for their ML algorithm compared to assess-ment with the Romhilt-Estes point system, Cornell voltage criteria or Sokolow-Lyon cri-teria (Figure 5). Four of the eight studies reported a specificity of equal to or better than the classic ECG criteria [32,34,36,42] (Figure 6). For some ML algorithms, specificity was higher than the classic ECG criteria, while others did not find a significant difference. Seven studies reported better AUC for their ML algorithm compared to an assessment with the Romhilt-Estes point system, Cornell voltage criteria or Sokolow-Lyon criteria (Figure 7). Several studies listed the important factors in their ML models. Ignoring the QRS voltage, Lin and Liu reported that there were other significant predictors of LVH, includ-ing age, heart rate, PR interval, uncorrected QT interval and QRS axis in Lead II [31]. Sys-tolic and diastolic BP values were in the top-40 predictors of LVH in the algorithm pro-posed by Naderi et al. [43]. Age and blood pressure were key predictors of LVH in the ML model of Sammani et al., along with P- and T-wave characteristics [41]. Age, waist circum-ference, different components of BP, history of hypertension, serum renin and antihyper-tensive treatment were the top predictors of LVH in the algorithm of Sabovcik et al. [37]. Figure 5. The sensitivity of the ML algorithms for LVH compared to standard ECG LVH criteria in studies that reported such data. Lin & Lui 2020 [31], Sparapani et al., 2019 [32], De la Gar-za-Sala-zar et al., 2020 [33], Kwon et al., 2020 [34], De la Gar-za-Salazar et al., 2021 [35], Khurshid et al., 2021 [36], Zhao et al., 2022 [40], Kokubo et al., 2022 [42], Liu et al., 2023 [44]. Figure 5. The sensitivity of the ML algorithms for LVH compared to standard ECG LVHcriteria in studies that reported such data. Lin & Lui 2020 [31], Sparapani et al., 2019 [32],De la Gar-za-Salazar et al., 2020 [33], Kwon et al., 2020 [34], De la Gar-za-Salazar et al., 2021 [35],Khurshid et al., 2021 [36], Zhao et al., 2022 [40], Kokubo et al., 2022 [42], Liu et al., 2023 [44].Bioengineering 2024, 11, x FOR PEER REVIEW 11 of 17 Figure 6. The specificity of the ML algorithms for LVH compared to standard ECG LVH criteria in studies that reported such data. Lin & Lui 2020 [31], Sparapani et al., 2019 [32], De la Gar-za-Salazar et al., 2020 [33], De la Gar-za-Salazar et al., 2021 [35], Khurshid et al., 2021 [36], Zhao et al., 2022 [40], Kokubo et al., 2022 [42], Liu et al., 2023 [44]. Figure 7. The accuracy of the ML algorithms for LVH compared to standard ECG LVH criteria in studies that reported such data. Lin & Lui 2020 [31], De la Gar-za-Salazar et al., 2020 [33], Kwon et al., 2020 [34], De la Gar-za-Salazar et al., 2021 [35], Kokubo et al., 2022 [42], Liu et al., 2023 [44]. i re 6. The specificity of the ML algorithms for LVH compared to standard ECG LVHcriteria in studies that reported such data. Lin & Lui 2020 [31], Sparapani et al., 2019 [32],De la Gar-za-Salazar et al., 2020 [33], De la Gar-za-Salaza et al., 2021 [35], Khurshid et al., 2021 [36],Zhao et al., 2022 [40], Kokubo et al., 2022 [42], Liu et al., 2023 [44].Bioengineering 2024, 11, 489 11 of 16Bioengineering 2024, 11, x FOR PEER REVIEW 11 of 17 Figure 6. The specificity of the ML algorithms for LVH compared to standard ECG LVH criteria in studies that reported such data. Lin & Lui 2020 [31], Sparapani et al., 2019 [32], De la Gar-za-Salazar et al., 2020 [33], De la Gar-za-Salazar et al., 2021 [35], Khurshid et al., 2021 [36], Zhao et al., 2022 [40], Kokubo et al., 2022 [42], Liu et al., 2023 [44]. Figure 7. The accuracy of the ML algorithms for LVH compared to standard ECG LVH criteria in studies that reported such data. Lin & Lui 2020 [31], De la Gar-za-Salazar et al., 2020 [33], Kwon et al., 2020 [34], De la Gar-za-Salazar et al., 2021 [35], Kokubo et al., 2022 [42], Liu et al., 2023 [44]. Figure 7. The accuracy of the ML algorithms for LVH compared to standard ECG LVH crite-ria in studies that reported such data. Lin & Lui 2020 [31], De la Gar-za-Salazar et al., 2020 [33],Kwon et al., 2020 [34], De la Gar-za-Salazar et al., 2021 [35], Kokubo et al., 2022 [42],Liu et al., 2023 [44].Several studies listed the important factors in their ML models. Ignoring the QRSvoltage, Lin and Liu reported that there were other significant predictors of LVH, includingage, heart rate, PR interval, uncorrected QT interval and QRS axis in Lead II [31]. Systolicand diastolic BP values were in the top-40 predictors of LVH in the algorithm proposed byNaderi et al. [43]. Age and blood pressure were key predictors of LVH in the ML model ofSammani et al., along with P- and T-wave characteristics [41]. Age, waist circumference,different components of BP, history of hypertension, serum renin and antihypertensivetreatment were the top predictors of LVH in the algorithm of Sabovcik et al. [37].4. DiscussionThis study demonstrates the wide variety of machine learning techniques that havebeen used to assess the presence of an increased left ventricular mass or cardiac hyper-trophy. It demonstrates the differences in sensitivity, specificity and predictive accuracybetween ML algorithms. It further identifies large differences in the input variables betweenalgorithms. These differences underscore the necessity to conduct an in-depth evaluation.The sources of datasets for left ventricular hypertrophy from the ECG in the literaturevaried widely between studies. Two studies derived data from military recruits, who wereessentially young men with a low prevalence of LVH [31,39]. There were three population-based studies, with two studies on the same UK database, with a greater prevalence ofLVH [32,36,37,43]. There were eight hospital-based studies, which had, on average, theoldest mean age and the highest prevalence of LVH, with the proportion of men rangingfrom 42 to 64% [33–35,38,40–42] and one military hospital with a predominance of men(90%) [44]. Overall, the proportion of men and women varied greatly but mainly becauseof the predominance of men in the studies of young military recruits and in a militaryhospital. Young male military recruits may not be generalizable to the general populationor to older patients admitted to hospital. The prevalence of LVH between studies rangedBioengineering 2024, 11, 489 12 of 16from 0.8 to 48% and may have influenced the precision of LVH detection. The majority ofstudies used echocardiograms to assess the prevalence of LVH, but the LVH criteria variedbetween studies in Asian or European populations.ML algorithms may be differentiated by the manner in which they select the boundarythat distinguishes different groupings. SVM was used by several groups [31,37,43]. TheSVM classifier can use linear or non-linear functions, although linear functions are usuallyselected. The decision boundary in this method is called the maximum margin classifier,maximum margin hyperplane or the maximum margin hyper plane [45]. Other studiesrelied on logistic regression [33], a simpler method that tries to maximize the conditionallikelihoods; however, it is more prone to outliers than SVMs, which mostly prioritize thepoints that are closest to the decision boundary. However, LR and SVM often yield similarresults [46]. Some studies used RF [37,39,43]. Random forests are a classification algorithmusing an ensemble of decision trees, such that each tree depends on the values of a randomvector sampled independently, and the generalizability depends on the strength of eachtree and the correlation between them [47]. In several clinical diagnosis conditions, RFshowed the highest accuracy followed by SVM [48].Angelaki et al. used SHAP (SHapley Additive exPlanations), a game theoretic ap-proach that connects optimal credit allocation with local explanations, using the classicShapley values from game theory and their related extensions [38]. A number of stud-ies used multiple ML algorithms [37,39]. Some investigators employed deep learningmethods [34,40,42]. The explosive growth of deep learning for ECG data led to the con-clusion that a hybrid architecture of a convolutional neural network and recurrent neuralnetwork ensemble yielded the best results [49]. However, there are some new challengesand problems related to interpretability, scalability, and efficiency, in addition to differencesin the perspectives of datasets and methods [49]. This hybrid combination has been usedin a few studies for LVH detection [34,42].Liu et al. reported both very high sensitivity and specificity. Usually, the higher a testsensitivity, the lower its specificity. They used detailed QRS analysis, but other studiesthat did not attain as a high a sensitivity and specificity also used detailed ECG signalanalysis [43]; for example, Zhao et al. had 36,350 ECG segments in their final dataset [40],and another ML algorithm used 552 amplitude and duration measurements per ECG [32].The findings of Liu et al. [44] showed both very high sensitivity and specificity, but this mayrelate to their decision that they had too few LVH cases “for designing a machine-learningmodel. Therefore, the beat segmentation method, Pan-Tompkins technique was performedto increase the ECG data amount to improve the detection performances” [44].The crucial test of the ML algorithms is the comparative ability to predict LVH. Thebest or highest sensitivity was the algorithm proposed by Liu et al. [44], followed by Linand Liu [31] and then by De la Garza-Salazar et al. [33]. If one wants a specific diagnosis,the highest specificity was found using the algorithm proposed by Sammani et al. [41],followed closely by that of Liu et al. [44] and then Khurshid et al. [36]. However, algorithmswith high specificity often have low sensitivity. Combining sensitivity and specificity usingROC curves suggests the best approach would be the algorithms of Angelaki et al. [38]followed by Kwon et al. [34]. Several studies compared different ML models to predictLVH [34,39,42]. The differences were usually not large. Two studies compared at least fourML approaches, and both found that ENN had the highest AUC; ENN offered a competitiveadvantage over other ML approaches [34,42]. Kokopo et al. developed an ensemble neuralnetwork (ENN) model, which consisted of a convolutional neural network (CNN) and adeep neural network (DNN) [42]. Kwan et al. used a deep neural network (DNN). Based onthe two studies that used ENN, this ML approach (ENN) offered a competitive advantageover other ML approaches [34,42].Comparisons with classical ECG criteria for LVH showed that ML algorithms were usu-ally more sensitive than the standard Cornell voltage [13], Cornell product [28], Sokolow-Lyons [9] or Romhilt-Estes criteria [29] for the detection of LVH. In contrast, generally, theML algorithms were not more specific than the classic criteria, as four ML algorithms wereBioengineering 2024, 11, 489 13 of 16no better and four were worse than these classic criteria for LVH. The ML algorithms ofSparapani et al. [32], Kokubo et al. [42], Kwan et al. [34] and Khurshid et al. [36] had aspecificity equal to the classical ECG criteria.A major theoretical issue with the ML algorithms for the detection of LVH is theuse of different kinds of input data. There are several lines of reasoning for the use ofML for LVH diagnosis. The first is whether ML can improve LVH detection based onQRS complexes and especially QRS voltage, which was historically the first attempt toelectrocardiographically identify LVH [9]. The second approach is to utilize all aspects ofthe ECG signal. This was embodied by the work of Romhilt and Estes [29], who added QRSaxis and ST-T waves to QRS voltage to identify LVH. As such, ML algorithms can point tothe classical approach to justify the inclusion of other ECG factors. The incorporation ofclinical factors becomes more problematic in ECG assessment. When age and history ofhypertension are included, sensitivity increases markedly, but is that a fair test of the use ofECGs in diagnosis? The addition of an extensive list of clinical and laboratory variablesfurther removes the question from the utility of the ECG but satisfies the question of howto more accurately predict the presence of LVH. For example, Sabovcˇik et al. inputted alarge number of clinical and laboratory variables, including blood count, blood glucose,lipids, renin activity, leptin, insulin, aldosterone and cortisol [37]. Zhao et al. included theinput variables of clinical factors, age, sex and medical history, as well as laboratory factors,like hemoglobin, PLT, lipids, creatinine sodium and potassium [40]. The inclusion of suchextensive clinical and laboratory data precludes the use of the ECG as a screening test forthe presence of LVH, as all the clinical and other laboratory data, which are usually notavailable, would have to be inputted to utilize the algorithms.Studies on machine learning-based prediction models have been criticized because ofpoor methodological quality and a high risk of bias [50]. The criticism relates to the frequentfailure ‘to report key information to help readers judge the methods and have a complete,transparent and clear picture of the . . .content of the model’ [51]. This criticism has somevalidity in the assessment of ML algorithms for the detection/diagnosis of LVH. Thesekinds of models, because of their complexity, have been labelled as a ‘black box’, certainlycompared to regression-based models that can be more recognizable [51]. For example, itis challenging to compare algorithms that state they are derived from 24 features, whichconsist of R peak and S valley amplitudes automatically obtained from the output of anECG signal [44] versus raw ECG data, with 5000 numbers from each of the 12 leads [34].Recognizing the limitations of each of the studies, it is worth discussing the implications ofthe results. First, ML algorithms can improve the sensitivity of the ECG for the detectionof LVH. Improving sensitivity is important for a screening technique, and the ECG fulfillsthat requirement. Second, simplicity warrants using an algorithm that only relies on ECGvariables to add to the ECG interpretation with respect to LVH. Third, algorithms that weredeveloped utilizing a neural network approach appear to offer a competitive advantageover other ML approaches.There are several limitations of this analysis that warrant discussion. First, the studiesusually utilized ML approaches from available ‘packages’. Wallace et al. cautioned that the“near-ubiquitous reliance on ‘out of bag’ approaches may provide ‘misleading results” [52].Second, many of the algorithms use ECG variables from most or all of the ECG leads, butin LVH detection, the QRS criteria from multiple leads often provide similar data [53].Third, not all publications provided the same outputs to compare accuracy, F1 or ROC data.Fourth, it is difficult to compare and select the ‘best’ ML algorithms when one algorithmemploys an extensive list of laboratory variables and another uses only ECG factors. One isleft with the question whether one approach would be better if it also included an extensivelist of laboratory tests.In summary, it is important to re-emphasize the potential of the ECG to identify LVHbecause LVH is a significant predictor of cardiovascular events [3,8,19,24] and becausea better approach for LVH detection would be an important contribution. Several MLalgorithms improve the sensitivity, but most do not improve specificity for LVH diagnosisBioengineering 2024, 11, 489 14 of 16compared to classical ECG criteria. Future research is needed to obtain a more standardizedapproach for the evaluation and comparison of all ML algorithms using the same datasetto determine the competitive advantage of each and identify the best one. In addition,the separation of LVH diagnosis into two stages—an ECG interpretation that uses an MLalgorithm and a second step with a simple application—can add further clinical variables.Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/bioengineering11050489/s1, Figure S1: Preferred ReportingItems for Reviews and Meta-Analysis (PRISMA).Funding: No funding to report. There was no sponsor or any contribution from a sponsor.Data Availability Statement: The data used are available in the literature.Conflicts of Interest: The author declares no conflicts of interest.AbbreviationsSVM Support vector machineLR logistic regressionRF Random ForestENN Ensemble neural networkCNN Convolutional neural networkDNN Deep neural networkAUC Area under the receiver operating curveReferences1. Levy, D.; Garrison, R.J.; Savage, D.D.; Kannel, W.B.; Castelli, W.P. 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left ventricular hypertrophy
electrocardiogram
machine learning
artificial intelligence
Searching for the Best Machine Learning Algorithm for the Detection of Left Ventricular Hypertrophy from the ECG: A Review
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